換気システムを備えた待避家屋におけるエアロゾル粒子の侵入、沈積および除去プロセスの実験室シミュレ－ションと評価

京都大学0048新制・課程博士博士(工学)甲第22764号工博第4763号新制||工||1745(附属図書館)京都大学大学院工学研究科都市環境工学専攻(主査)教授 米田 稔, 教授 橋本 訓, 准教授 福谷 哲学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDFA

A Digital Maskless Photolithographic Patterning Method for DNA Based UV Photocleavable PEGDA Hydrogels with A Camphorquinone-Triethanolamine Photoinitiator

The prerequisite of establishing Ultra Violet photo-responsive soft materials is to find out a suitable photoinitiator triggered by visible light. In this thesis, we introduced a blue-light-absorbing (peak absorbance at 470nm) photoinitiator system, camphorquinone and triethanolamine, to overcome the drawbacks of widely used UV photoinitiators that are incompatible with biomolecules like DNA and will crosstalk with UV photo-cleavable chemistry we utilized. We optimized the formulation to photopattern PEGDA-DNA co-polymerized hydrogel for high pattern fidelity and mechanical property to be isolated from microfluidic devices. Digital maskless photolithography enables the immobilization of acrylate-modified oligonucleotides within patterned hydrogels at a dimension of tens of microns. To demonstrate the control of UV photo-cleavage, we used an acrylate-modified DNA strand containing a 1-(2-nitrophenyl) ethyl spacer to selectively cleave and release oligonucleotide segments from a region inside a PEGDA hydrogel. This UV responsive co-PEGDA-DNA hydrogel fabrication approach can be used in performing pattern-transformation algorithms such as edge detection or as a trigger for downstream sequential release cascades on micron scale. Committee members: Advisor: Prof. Rebecca Schulman Reader: Prof. Sung Hoon Kan

Screening for electrically conductive defects in thin functional films using electrochemiluminescence

Multifunctional thin films in energy-related devices often must be electrically insulating where a single nanoscale defect can result in complete device-scale failure. Locating and characterizing such defects presents a fundamental problem where high-resolution imaging methods are needed to find defects, but imaging with high spatial resolution limits the field of view and thus the measurement throughput. Here, we present a novel high-throughput method for detecting sub-micron defects in insulating thin films by leveraging the electrochemiluminescence (ECL) of luminol. Through a systematic study of reagent concentrations, buffers, voltage, and excitation time, we identify optimized conditions at which it is possible to detect features with areas ~500 times smaller than the area interrogated by a single pixel of the camera, showing high-throughput detection of sub-micron defects. In particular, we estimate the minimum detectable features to be lines as narrow as 2.5 nm in width and pinholes as small as 35 nm in radius. We further explore this method by using it to characterize a nominally insulating phenol film and find conductive defects that are cross-correlated with high-resolution atomic force microscopy to provide feedback to synthesis. Given the inherent parallelizability and scalability of this assay, it is expected to have a major impact on the automated discovery of multifunctional films.Comment: 24 pages, 5 figures, submitted to Langmui

SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control

Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the development of safety-oriented intersection control. However, existing studies on adaptive traffic signal control using reinforcement learning technologies have focused mainly on minimizing traffic delay but neglecting the potential exposure to unsafe conditions. We, for the first time, incorporate road safety standards as enforcement to ensure the safety of existing reinforcement learning methods, aiming toward operating intersections with zero collisions. We have proposed a safety-enhanced residual reinforcement learning method (SafeLight) and employed multiple optimization techniques, such as multi-objective loss function and reward shaping for better knowledge integration. Extensive experiments are conducted using both synthetic and real-world benchmark datasets. Results show that our method can significantly reduce collisions while increasing traffic mobility.Comment: Accepted by AAAI 2023, appendix included. 9 pages + 5 pages appendix, 12 figures, in Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI'23), Feb 202

RFID-Based Indoor Spatial Query Evaluation with Bayesian Filtering Techniques

People spend a significant amount of time in indoor spaces (e.g., office buildings, subway systems, etc.) in their daily lives. Therefore, it is important to develop efficient indoor spatial query algorithms for supporting various location-based applications. However, indoor spaces differ from outdoor spaces because users have to follow the indoor floor plan for their movements. In addition, positioning in indoor environments is mainly based on sensing devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot apply existing spatial query evaluation techniques devised for outdoor environments for this new challenge. Because Bayesian filtering techniques can be employed to estimate the state of a system that changes over time using a sequence of noisy measurements made on the system, in this research, we propose the Bayesian filtering-based location inference methods as the basis for evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two novel models, indoor walking graph model and anchor point indexing model, are created for tracking object locations in indoor environments. Based on the inference method and tracking models, we develop innovative indoor range and k nearest neighbor (kNN) query algorithms. We validate our solution through use of both synthetic data and real-world data. Our experimental results show that the proposed algorithms can evaluate indoor spatial queries effectively and efficiently. We open-source the code, data, and floor plan at https://github.com/DataScienceLab18/IndoorToolKit

Response characteristics of root to moisture change at seedling stage of Kengyilia hirsuta

Kengyilia hirsuta is an important pioneer plant distributed on the desertified grassland of the Qinghai-Tibet Plateau. It has strong adaptability to alpine desert habitats, so it can be used as a sand-fixing plant on sandy alpine land. To study the response mechanisms of root morphological and physiological characteristics of K. hirsuta to sandy soil moisture, 10%, 25% and 40% moisture levels were set up through potted weighing water control method. The biomass, root-shoot ratio, root architecture parameters, and biochemical parameters malondialdehyde, free proline, soluble protein, indole-3-acetic acid, abscisic acid, cytokinin, gibberellin, relative conductivity and antioxidant enzyme activities were measured in the trefoil stage, and the response mechanisms of roots at different moisture levels were analyzed. The results showed that with the increase of soil moisture, root morphological indexes such as root biomass, total root length, total root volume and total root surface increased, while the root topological index decreased continuously. The malondialdehyde content, relative conductivity, superoxide dismutase activity, peroxidase activity, catalase activity, free proline content, soluble protein content, abscisic acid content and cytokinin content at the 25% and 40% moisture levels were significantly decreased compared with the 10% level (P< 0.05). Thus, the root growth of K. hirsuta was restricted by the 10% moisture level, but supported by the 25% and 40% moisture levels. An artificial neural network revealed that total root length, total root surface area, root link average length, relative conductivity, soluble protein, free proline and moisture level were the key factors affecting root development. These research results could contribute to future agricultural sustainability